Systems and methods for selectively communicating with a user via a System 1 or System 2 channel

Abstract

To provide systems and methods.SOLUTION: Systems and methods for selecting a communication channel based on the type of thinking used by a user for a task are disclosed. The systems and methods include the steps of: determining that a user is using System 1 type thinking or System 2 type thinking, based on one or more characteristics of the task, one or more characteristics of the user, and a user's state based on physiological response data from one or more physiological sensors monitoring the user; and implementing a communication channel to be used for the task that corresponds to the determined type of thinking that the user is using for the task.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] This specification generally relates to systems and methods for determining whether to use System 1 type of thinking or System 2 type of thinking when a user is engaged in a task, and methods for selectively communicating with a user via a communication channel corresponding to System 1 type of thinking or System 2 type of thinking.

Background Art

[0002] Research on human logical thinking suggests that most of the human logical thinking ability can be approximated by a dual - process model that distinguishes between rapid automatic intuition and slower, more deliberate analytical processing. These two are typically referred to as System 1 type of thinking and System 2 type of thinking, respectively. Generally, System 2 type of thinking has the advantage of cognitive power in terms of accuracy and generalizability, while System 1 type of thinking has advantages in terms of cognitive processing cost and speed from the perspectives of working memory and attention. By distinguishing between System 1 type of thinking and System 2 type of thinking, it becomes possible to adjust human - interaction systems to communicate more effectively with users.

[0003] Therefore, there is a need for systems and methods for determining whether to use System 1 type of thinking or System 2 type of thinking when a user is engaged in a task.

Summary of the Invention

[0004] In an embodiment, a method for selecting a communication channel based on the thinking type used by a user for a task is disclosed. The method includes determining, based on one or more characteristics of the task, one or more characteristics of the user, and the user's state based on physiological response data from one or more physiological sensors that monitor the user, that the user is using System 1 type of thinking or System 2 type of thinking for the task; and implementing a communication channel to be utilized for the task corresponding to the determined thinking type used by the user for the task.

[0005] In some embodiments, a system for selecting a communication channel based on the thinking type used by a user for a task is disclosed. The system includes one or more physiological sensors configured to detect one or more characteristics of the user, and an electronic control unit communicatively coupled to the one or more physiological sensors. The electronic control unit is configured to determine, based on one or more characteristics of the task, one or more characteristics of the user, and physiological response data from the one or more physiological sensors, that the user is using System 1 type of thinking or System 2 type of thinking for the task, and to implement a communication channel to be utilized for the task corresponding to the determined thinking type used by the user for the task.

[0006] In some embodiments, a system for selecting a communication channel based on the type of thinking a user uses for a task is disclosed. The system includes one or more physiological sensors configured to detect one or more characteristics of the user, and an electronic control unit communicatively coupled to the one or more physiological sensors. The electronic control unit implements a machine learning model and receives, as inputs to the machine learning model, one or more characteristics of Task 1, one or more characteristics of the user related to the task, and a user state based on one or more characteristics of the user detected by the one or more physiological sensors. Using the machine learning model, it predicts whether the user is using System 1 type of thinking or System 2 type of thinking for the task based on one or more characteristics of the task, one or more characteristics of the user, and the user state, and is configured to implement a communication channel to be utilized for the task corresponding to the determined type of thinking the user is using for the task.

[0007] These and additional features provided by the embodiments described herein will be more fully understood in consideration of the following detailed description in conjunction with the drawings.

[0008] The embodiments described in the drawings are exemplary in nature and are not intended to limit the subject matter defined by the claims. The following detailed description of the exemplary embodiments can be understood when read in conjunction with the following drawings, in which similar structures are labeled with similar reference numerals.

Brief Description of the Drawings

[0009]

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[0010] The embodiments described in this specification relate to systems and methods for determining whether a user uses System 1 type of thinking or System 2 type of thinking when engaged in a task. System 1 type of thinking means a thinking process in which the user uses automatic, intuitive, and unconscious thinking. That is, System 1 type of thinking generally requires little energy or attention. However, when the user uses System 1 type of thinking, the response tends to be influenced by preconceptions because little or no analytical thinking is used. Furthermore, System 1 type of thinking constantly creates impressions, intuitions, and judgments based on our daily routines. That is, when faced with a decision, humans automatically use System 1 type of thinking. Therefore, System 1 type of thinking is highly influential and can guide most everyday decisions. However, unlike System 1 type of thinking, System 2 type of thinking requires energy and attention to think through options and information. System 2 type of thinking filters out automatic intuitions and preconceptions and makes a rational choice based on reliable information. That is, System 2 type of thinking is a slow and controlled analytical thinking method dominated by judgment. Furthermore, System 2 type of thinking typically occurs when faced with a new situation or when the user consciously makes an effort to engage in the details of a task.

[0011] However, the determination of whether a user uses System 1 type of thinking or System 2 type of thinking when engaged in a task is not based solely on the specificity of the task itself. As will be described in more detail in this specification, the determination involves the specificity of the user performing the particular task. For example, an inexperienced user may use System 2 type of thinking for a particular task, while an experienced user performing the same task may use System 1 type of thinking.

[0012] This embodiment discloses a system and method configured to collect and analyze information from at least three specific categories to determine whether a user is currently using or will use either System 1 type of thinking or System 2 type of thinking for a specific task. The three factors include the characteristics of the task, the characteristics of the user, and the state of the user. As described in more detail herein, the analysis of the information related to the three factors is performed by a computer device such as an electronic control unit. The electronic control unit is configured to receive sensor data from one or more sensors and information from one or more data storage devices such as a database related to one or more of the three factors. The electronic control unit analyzes the data and information using one or more algorithms and / or machine learning models described in more detail herein.

[0013] In some embodiments, the systems and methods described herein can be implemented to enhance or improve the interaction and / or communication between a system that deploys one task, such as a vehicle navigation system configured to provide navigation information, and a user. For example, when the system determines or receives a classification that the user of the system is using either System 1 type of thinking or System 2 type of thinking, the system then determines the best way to provide information for the user to complete the task that works with the thinking type the user is currently using. For example, when it is determined whether the user mainly uses System 1 type of thinking or System 2 type of thinking to complete a specific task, a communication channel that is consistent with the thinking type mainly used by the user to complete this specific task is selected and implemented.

[0014] When a user is using System 1 type of thinking, the information provided by the system to the user can be in a visual form (such as what is called the visual mode in this specification), such as in the form of photos, graphs, and other non-verbal types of information. For example, when a user using System 1 type of thinking conducts an investigation on climate change, the system is considered to provide information using visual materials such as charts and graphs that illustrate climate change. Conversely, when a user is using System 2 type of thinking, the system can provide more linguistic information, such as text information regarding climate change (such as what is called the text mode in this specification) and a numerical spreadsheet regarding climate change. Since the thinking type can change rapidly, for example when the task type changes, the communication channel should similarly change just as rapidly.

[0015] As described in more detail herein, once it is determined that a user is using either System 1 type of thinking or System 2 type of thinking, a vehicle system such as a navigation system or another vehicle or non-vehicle-based system can be configured to use a dialogue and / or communication system and method that is consistent with the type of thinking the user is using for that task. In this particular case, for example, a driver operating a vehicle may be involved in a number of tasks to varying degrees. Further, the environment in which the driver is operating is changing rapidly, which can cause the task to change or the focus to shift from the original task, such as receiving a set of visualized directions from the navigation system. Therefore, if the driver needs to refocus on the road while receiving a set of visualized directions, the visualization of the directions should be changed to an auditory channel.

[0016] In the following, these systems and methods will be described in more detail with reference to the drawings in which like numbers mean like structures.

[0017] Referring now to FIG. 1, a system 100 is depicted for determining whether a user uses System 1 type of thinking or System 2 type of thinking when engaging in a task. The system 100 includes an electronic control unit 130. The electronic control unit 130 includes a processor 132 and a memory component 134. The system 100 also includes a communication bus 120, one or more input devices 136, one or more cameras 138, an eye tracking system 140, a lighting device 141, one or more physiological sensors 142, a speaker 144, a steering wheel system 146, a display 148, a data storage component 150, and / or a network interface hardware 170. The system 100 may be communicatively coupled to a network 180 using the network interface hardware 170. The components of the system 100 are communicatively coupled to each other via the communication bus 120.

[0018] It is understood that the embodiments depicted and described herein are not limited to the components or configurations depicted and described with respect to FIG. 1, but rather FIG. 1 is for illustrative purposes only. The various components of the system 100 and their interactions are detailed herein.

[0019] The communication bus 120 may be formed from any medium having the ability to transmit signals, such as conductive wires, conductive traces, optical waveguides, etc. The communication bus 120 may similarly mean the spread through which electromagnetic radiation and its corresponding electromagnetic waves traverse. Further, the communication bus 120 may be formed from a combination of media having the ability to transmit signals. In one embodiment, the communication bus 120 includes a combination of conductive traces, wires, connectors, and buses that cooperate to enable the transmission of electrical data signals to components such as processor 132, memory, sensors, input devices, output devices, and communication devices. Thus, the communication bus 120 may include a bus. Further, it is pointed out that the term "signal" means waveforms (such as electrical, optical, magnetic, mechanical, or electromagnetic) such as DC, AC, sine waves, triangular waves, square waves, vibrations, etc. that have the ability to travel through a medium. The communication bus 120 communicatively couples the various components of the system 100. As used herein, the term "communicatively coupled" means that the coupled components have the ability to exchange signals with each other, such as via an electrical signal through a conductive medium, an electromagnetic signal through air, an optical signal through an optical waveguide, etc.

[0020] The electronic control unit 130 can be any device or combination of components including a processor 132 and a memory component 134. The processor 132 of the system 100 can be any device having the ability to execute a set of machine-readable instructions stored within the memory component 134. Thus, the processor 132 can be an electric controller, an integrated circuit, a microchip, a field programmable gate array, a computer or any other computer device. The processor 132 is communicatively coupled to the other components of the system 100 by a communication bus 120. Thus, the communication bus 120 can communicatively couple any number of processors 132 to each other and enable the components coupled to the communication bus 120 to operate within a distributed computing environment. Specifically, each component can operate as a node capable of transmitting and / or receiving data. Although the embodiment depicted in FIG. 1 includes a single processor 132, other embodiments can include two or more processors 132.

[0021] The memory component 134 of system 100 is coupled to communication bus 120 and communicatively coupled to processor 132. The memory component 134 can be implemented as, for example, RAM, ROM, flash memory, a hard drive, or any non - transitory computer - readable memory having the ability to store machine - readable instructions that can be accessed and executed by processor 132. The set of machine - readable instructions can include logic or algorithms written in any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL), such as machine language that can be directly executed by processor 132, or assembly language, object - oriented programming (OOP), script language, microcode, etc., that can be compiled or assembled into machine - readable instructions and stored within memory component 134. Alternatively, the set of machine - readable instructions can be written in a hardware description language (HDL), e.g., logic implemented via either a field - programmable gate array (FPGA) configuration or an application - specific integrated circuit (ASIC) or their equivalents. Thus, the functionality described herein can be implemented in any conventional computer programming language, as pre - programmed hardware elements, or as a combination of hardware and software components. Although the system 100 depicted in FIG. 1 includes a single memory component 134, other embodiments can include two or more memory components 134.

[0022] System 100 includes one or more input devices 136. The one or more input devices 136 can be any device configured to enable a user to input information into the system. For example, the one or more input devices 136 can include a keyboard, a mouse, a stylus, a scanner, a gesture camera, a microphone, and the like. System 100 can similarly include one or more cameras 138. The one or more cameras 138 can be communicatively coupled to the communication bus 120 and the processor 132. The one or more cameras 138 can be any device having an array of sensing devices (e.g., pixels) capable of detecting radiation in the ultraviolet wavelength range, the visible light wavelength range, or the infrared wavelength range. The one or more cameras 138 can have any resolution. The one or more cameras 138 can be, for example, an omnidirectional camera or a panoramic camera. In some embodiments, one or more optical components, such as mirrors, fisheye lenses, or any other type of lens, can be optically coupled to each of the one or more cameras 138. The one or more cameras 138 can capture image data or video data of the vehicle's environment.

[0023] In some embodiments, system 100 can be implemented within vehicle 110 (FIG. 3) to enhance the interaction and / or communication between one or more vehicle systems and the driver. For example, once it is determined whether the user is using System 1 type of thinking or System 2 type of thinking, as described in more detail herein, the vehicle system or another system can be configured to use a system and method of interaction and / or communication that is consistent with the type of thinking the user is using for that task. For example, one or more cameras 138 can be implemented by system 100 to capture events occurring in the environment around the driver to provide information related to the user's state and / or the characteristics of the task.

[0024] System 100 can include an eye-tracking system 140 for tracking the eye movements and / or fixations of a subject. The eye-tracking system 140 can include one or more cameras 138 and / or an array of infrared detectors positioned to examine one or more eyes of the subject. The eye-tracking system 140 can similarly include or be communicatively coupled to an illumination device 141, which may be an infrared or near-infrared emitter. The illumination device 141 can emit infrared or near-infrared light, which reflects off a portion of the eye to create a profile that is more easily detectable than the visible light reflection from the eye for eye-tracking purposes. In some embodiments, the eye-tracking system 140 can similarly be configured to determine the pupil dilation or constriction of the user's eyes.

[0025] The eye-tracking system 140 can be spatially oriented within the environment and can generate a fixation direction vector. One of various coordinate systems, such as a user coordinate system (UCS), can be implemented. For example, the UCS has its origin at the center of the front surface of the fixation tracker. The fixation direction vector can be defined in relation to the location of the origin with the origin defined at the center of the front surface of the eye-tracking system 140 (e.g., the eye-tracking camera lens). Further, when spatially orienting the eye-tracking system 140 within the environment, all other objects, including the one or more cameras 138, can be located in relation to the location of the origin of the eye-tracking system 140. In some embodiments, the origin of the coordinate system can be defined at one location on the subject, such as at one spot between the subject's eyes. Regardless of the location of the origin of the coordinate system, the calibration process by the eye-tracking system 140 can be used to calibrate the coordinate system to collect eye-tracking data, pupil dilation data, and / or eye movement information that can be used to determine the user's state, task characteristics, and / or user characteristics.

[0026] Referring further to FIG. 1, system 100 may further include one or more physiological sensors 142. The one or more physiological sensors 142 may be communicatively coupled to communication bus 120 and electronic control unit 130. The one or more physiological sensors 142 may be any device having the ability to monitor and capture the physiological state of a human body, such as the driver's stress level, through monitoring of the heart's electrical activity, skin conductance, respiration, etc. The one or more physiological sensors 142 include sensors configured to measure physical events such as heart rate changes, skin potential (EDA), muscle tension, and cardiac output. The one or more physiological sensors 142 can monitor brain waves through electroencephalography EEG, skin potential through skin conductance response SCR, and electrodermal response GSR, cardiovascular measurements such as heart rate HR; beats per minute BPM; heart rate variability HRV; vasomotor activity, muscle activity through electromyogram EMG, changes in pupil diameter associated with thoughts and emotions through pupillometry (e.g., pupillometry data), eye movements recorded through electrooculogram EOG and gaze direction methods, and cardiac mechanics recorded through impedance cardiography, or other physiological measurements.

[0027] The physiological sensor 142 can generate physiological response data that can be used to train or evolve the neural network 400 for the purpose of determining the user's state. The physiological response data of the subject can indicate the user's state. For example, the physiological sensor 142 may be able to detect user characteristics that indicate the user's stress level, fatigue level, and / or health status. In some cases, the physiological response data can indicate whether the user is performing multiple tasks or is concentrating on a particular task. For example, physiological response data indicating rapid heartbeat, dilated pupils, rapid eye movement, and / or the presence of sweat may indicate that the user has a high stress level. As another example, physiological response data indicating lackluster eye movement and / or the presence of discoloration of the eyes or around the eyes may indicate that the user is tired. Moreover, physiological response data related to the user's body temperature, blood oxygen concentration, heart rate (e.g., heart rate during activity or at rest), blood pressure, respiratory rate, oxygen saturation level, etc. indicates the user's health status.

[0028] The system 100 may similarly include a speaker 144. The speaker 144 (i.e., the audio output device) is coupled to the communication bus 120 and communicatively coupled to the electronic control unit 130. The speaker 144 converts an audio message as a signal from the processor 132 of the electronic control unit 130 into mechanical vibrations that produce sound. For example, the speaker 144 can provide information to the user in an audible form. The audible information may be used independently or in combination with visual images, text data, etc. However, it should be understood that in other embodiments, the system 100 may not include the speaker 144.

[0029] The steering wheel system 146 is coupled to the communication bus 120 and communicatively coupled to the electronic control unit 130. The steering wheel system 146 may include a plurality of sensors such as a physiological sensor 142 positioned within the steering wheel. Further, the steering wheel system 146 may include a motor or component to provide haptic feedback to the driver. For example, the steering wheel system 146 may be configured to provide variable-intensity vibrations through the steering wheel as a communication or warning means to the driver.

[0030] System 100 may include a display 148 for presenting information to the user in a visual format. The display 148 can be a heads-up display system, a navigation display, a smartphone or computer device, a television, etc. The display 148 can include any medium having the ability to transmit light output such as, for example, a cathode ray tube, a light emitting diode, a liquid crystal display, a plasma display. The display 148 can similarly include one or more input devices. The one or more input devices can be any device having the ability to convert a user's contact into a data signal transmissible on the communication bus 120, such as, for example, a button, a switch, a knob, a microphone. In some embodiments, the one or more input devices include a power button, a volume button, a start button, a scroll button, etc. One or more input devices can be provided to enable the user to interact with the display 148 to perform menu navigation, selection, preference setting, and other functionality described herein. In some embodiments, the input devices include pressure sensors, touch sensor regions, pressure strips.

[0031] The data storage component 150 communicatively coupled to the system 100 can be a volatile and / or non-volatile digital storage component, and thus can include random access memory (SRAM, DRAM, and / or other types of random access memory), flash memory, registers, compact discs (CDs), digital versatile discs (DVDs), and / or other types of storage components. The data storage component 150 can be present locally to and / or remotely from the system 100 and can be configured to store one or more data such as one or more characteristics of one or more tasks 152, one or more characteristics of one or more users 154, etc.

[0032] One or more characteristics of one or more tasks 152 may include characteristics such as task type. Task types may include incidental tasks, routine tasks, projects, reactive tasks, problem-solving tasks, decision-making tasks, planning tasks, collaborative or independent tasks, creative tasks, and the like. Each task type can be classified as a task involving System 1 type of thinking or System 2 type of thinking. However, task type alone cannot indicate the type of thinking the user implements. When determining whether one or more characteristics of a task indicate that the task the user uses involves System 1 type of thinking or System 2 type of thinking, other characteristics can be considered, such as the typical length of time required to complete the task, the number of steps involved in completing the task, which senses such as vision, hearing, taste, or touch are involved in completing the task, and / or whether the task involves processing of words, textual information, sounds, and / or images. For example, tasks with only a few steps, tasks that can be completed in a short time, tasks involving processing of visual and / or audible information, etc., generally cause the user to engage System 1 type of thinking. On the other hand, for example, tasks with more steps, tasks that take several minutes or hours to complete, tasks involving processing of words and / or textual information, etc., generally cause the user to engage System 2 type of thinking. Thus, by analyzing one or more characteristics of a task, the system and method can determine whether the characteristics of the task indicate a task involving System 1 type of thinking or a task involving System 2 type of thinking. As described in more detail herein, the determination based on one or more characteristics of a task as to whether the user is using System 1 type of thinking or System 2 type of thinking for a particular task can be a binary decision (e.g., System 1 or System 2), or a prediction defined by the likelihood that the user is using System 1 type of thinking or System 2 type of thinking.

[0033] One or more characteristics of one or more users 154 can include characteristics such as the experience level of the user performing the task, the number of times the user has completed a particular task or similar tasks in the past, the time the user has previously spent solving the same or similar tasks, whether the user is proficient in a series of specific skills that correlate with one or more characteristics of the task, and the frequency with which the user interacts with a particular task. One or more characteristics of one or more users 154 can be stored as a user profile in the data storage component 150 and / or the memory component 134 of the electronic control unit 130. The user profile can evolve based on past interactions the user has had with the system in completing tasks. For example, from previous interactions, the system can learn how the user solves tasks, whether the user can complete tasks in a timely or efficient manner, etc. In some embodiments, the user profile can be developed independently of past interactions with the task. For example, the system can prompt the user to complete surveys, questions, or simple cognitive tasks that collect information the user directly provides, such as the user's education level or skills, prompts that evaluate the user's set of skills through a rating process, prompts that obtain the user's comfort level with a particular type of task through a rating process, etc. The process of collecting information about the user can be combined with monitoring of the user's physiological state, for example, to determine whether the user feels stress when faced with a particular category of tasks or topics. Such information can, in part, indicate regarding which thought types the user uses when dealing with a particular task.

[0034] For example, an experienced user in solving or completing a particular task may not exhibit a high stress level, unlike those who are not familiar with the particular task. Moreover, a user who is proficient in a particular task is more likely to use type 1 thinking, as opposed to those who are not familiar with the particular task and are more likely to use type 2 thinking. As described in more detail herein, the determination of whether a user is using type 1 thinking or type 2 thinking for a particular task, based on one or more characteristics of one or more users, can be a binary decision (e.g., type 1 or type 2) or a prediction defined by the likelihood that the user is using type 1 thinking or type 2 thinking.

[0035] The data storage component 150 can similarly include one or more predefined matrices that link specific tasks, the type of thinking that the user uses for that specific task, and a communication channel 156 that defines the communication channel or method to be used. For example, a task such as navigation within a vehicle can be one task defined within the communication channel 156 data set. The matrix for the navigation task within the vehicle can be linked to multiple communication channels based on the type of thinking that the user involved in the navigation task within the vehicle is using. For example, if the user is exercising System 1 type of thinking, the matrix can define that the communication channel for providing instructions to the user includes a simplified visual cue (e.g., an arrow indicating the direction of an upcoming turn) and / or a simplified audible cue. Alternatively, if the user is exercising System 2 type of thinking, the matrix can define the communication channel to provide more detailed audible instructions and / or more detailed images for upcoming vehicle operations such as a series of consecutive turns approaching, because the driver is highly likely to want to plan some intermediate lane change or an unplanned stop while exercising System 2 type of thinking. The more detailed images can include a visualization of an overview map of the environment around the vehicle with the current location of the vehicle and / or the highlighted route drawn on top.

[0036] System 100 can further include a database that includes different ways of conveying the same type of information. For example, the database can include a particular type of information and two different types of conveyance for it (for example, one type focused on System 1 and another type focused on System 2). For example, in the context of a task of conducting a survey, it is conceivable that the system can have a database with information types such as average temperature changes over the past 50 years. The database can similarly have two different ways of conveying information, for example, through a graph illustrating the temperature change or a paragraph explaining the temperature change. Based on the type of thinking the user is engaged in, System 100 can select an appropriate way to convey the same information to the user, for example, either through a graph (for example, for System 1 type of thinking) or a documented paragraph (for example, for System 2 type of thinking).

[0037] Still referring to FIG. 1, System 100 can similarly include network interface hardware 170 communicatively coupled to electronic control unit 130 via communication bus 120. Network interface hardware 170 can include any wired or wireless networking hardware, such as a modem, a LAN port, a Wi-Fi card, a WiMax card, mobile communication hardware, and / or other hardware for communicating with network 180 and / or other devices and systems. For example, System 100 can be communicatively coupled to network 180 using network interface hardware 170.

[0038] Turning now to FIG. 2, there is shown an information flow diagram 200 for determining whether a user uses System 1 type of thinking or System 2 type of thinking when engaged in a single task. The information flow diagram 200 illustrates information sources that can be used by analysis modules 210-240 implemented by the electronic control unit 130. The system and method can be implemented using an electronic control unit 130 (FIG. 1) including one or more processors 132 (FIG. 1). The electronic control unit 130 receives information regarding task characteristics, user characteristics, and user state. The electronic control unit 130 may be in communication with one or more input devices to provide information regarding task characteristics, user characteristics, and user state.

[0039] The input device can be one or more sensors, such as camera 138, one or more physiological sensors 142A - 142I, or another type of sensor capable of providing information about the user's state. For example, regarding the user's state, the sensor can detect the user's stress level (e.g., based on rapid heartbeats, dilated pupils, rapid eye movements, presence of sweat, etc.), fatigue level (e.g., based on eye movements, presence of bags under the eyes, etc.), health status (e.g., based on body temperature, blood oxygen concentration, heartbeats, blood pressure, etc.), and whether the user is performing multiple tasks. FIG. 2 depicts various types of physiological sensors 142A - 142I. As non - limiting examples, physiological sensors 142A - 142I can include an electrocardiogram sensor 142A, a body temperature sensor 142B, a heart rate sensor 142C, a pupil dilation sensor 142D (e.g., implemented using one or more cameras 138 and / or a gaze tracking system 140), a gaze tracker 142E (e.g., the gaze tracking system 140), a sweating sensor 142F, an oxygen sensor 142G, a blood pressure sensor 142H, or a respiratory rate sensor 142I. These are merely some exemplary physiological sensors 142A - 142I that can be implemented within the system 100 to monitor various physiological conditions of the user. Physiological sensors 142A - 142I can be coupled to the user via a wearable device 149 (FIG. 3), embedded in a sheet or floor to monitor the user in contact with the sheet or floor, or configured to monitor the user from a certain distance when the user is within the detection range of one or more physiological sensors 142A - 142I. Physiological sensors 142A - 142I generate an electrical signal in response to the conditions they are configured to monitor and transmit this electrical signal to the electronic control unit 130 for analysis.

[0040] In addition to the sensors, system 100 can also obtain information including one or more characteristics of the task and one or more characteristics of the user from data storage component 150, input devices such as touch display 148, input devices 136, one or more cameras 138, etc. The electronic control unit 130 requests and / or receives information related to one or more characteristics of one or more tasks. Such information can include the type of task the user is currently engaged in, the typical time duration required to complete the task, the number of steps involved in completing the task, which senses such as vision, hearing, taste, or touch are involved in completing the task, whether the task involves the processing of words, character information, sounds, and / or images, etc.

[0041] Furthermore, system 100 obtains information about the user involved in the task. For example, one or more characteristics of one or more users 154 can include the user's experience level in performing the task, the number of times the user has completed a specific task or similar tasks in the past, the time the user has previously spent in solving the same or similar tasks, whether the user is proficient in a series of specific skills correlated with one or more characteristics of the task, the frequency with which the user interacts with a specific task, etc.

[0042] As depicted in FIG. 2, the electronic control unit 130 is configured with one or more analysis modules (e.g., user state module 210, task characteristic module 220, and user characteristic module 230) for the purpose of determining, for a particular task, which type of thinking, system 1 type or system 2 type, the user is currently using or will use in the future, by analyzing data from at least three specific categories (e.g., one or more characteristics of the task, one or more characteristics of the user, and the user's state). The electronic control unit 130 analyzes the physiological response data generated by the physiological sensors 142A - 142I to determine the user's state. The user's state can be multifaceted. That is, in order to determine the impact of the task on the user, the user's baseline state may be required. For example, a person in poor physical condition may naturally have a higher heart rate or blood pressure than normal, which may not indicate the user's stress level but rather be a result of the user's current health. Thus, the user state module 210 used by the electronic control unit 130 may be configured to determine the user's health state that can then be used as a baseline in determining one or more characteristics of the user that manifest as a response to engaging in the task. For example, these characteristics may include stress level, fatigue level, etc. Based on the user's state determined based on one or more physiological sensors configured to detect one or more characteristics of the user, the electronic control unit 130 can determine whether the user's state is correlated with that of a person who is engaging system 1 type of thinking or a person who is engaging system 2 type of thinking. As a non - limiting example, generally, the more the user is under stress, fatigue, or in a poor health state, the higher the likelihood of engaging system 1 type of thinking. Conversely, when calm, well - rested, and in a better health state, the user has a higher likelihood of engaging system 2 type of thinking. A person performing multiple tasks has a higher likelihood of engaging system 1 type of thinking. However, the user's state does not independently indicate the type of thinking the user engages in for a particular task.

[0043] Therefore, the electronic control unit 130 may similarly be in communication with one or more sensors (such as one or more cameras 138 and / or input devices such as touch display 148) configured to collect information regarding the characteristics of the user task and / or the characteristics of the user, and / or one or more databases (such as data storage component 150) that store information regarding the characteristics of the user task and / or the characteristics of the user. The electronic control unit 130 may further be configured with a task characteristic module 220 implemented to analyze one or more characteristics of the task to determine whether the task is one that the user is likely to implement System 1 type of thinking for essential completion or one that is likely to implement System 2 type of thinking. That is, the electronic control unit 130 can receive information related to a specific task from a database that stores information about the task. For example, one or more characteristics of the task may include the type of task, the typical length of time it takes to complete the task, the number of steps involved in completing the task, which senses (such as vision, hearing, taste, or touch) are involved in completing the task, and / or whether word / character information or sound / image processing is involved in the task.

[0044] It is also pointed out that the electronic control unit 130 may similarly include a process for determining which task the user is currently engaged in. In some embodiments, for the electronic control unit 130, the determination of which task the user is currently engaged in is simply a function of querying the operating state of the system to determine which functionality is currently being utilized. For example, in the context of a vehicle, the system 100 may be implemented as part of the vehicle-to-user interface, and the currently active vehicle system may be a navigation system. Thus, the electronic control unit 130 can determine that the user is currently engaged in vehicle navigation rather than communicating with another person via phone or multimedia messaging when these systems are not currently active.

[0045] Referring back to the analysis of one or more characteristics of a task to determine whether the task is one for which the user is likely to implement System 1 type of thinking for completion or one for which the user is likely to implement System 2 type of thinking, the task characteristic module 220 can be preconfigured with multiple pointers to make a determination or prediction about which type of thinking the user is likely to engage in for a particular task. As a non-limiting example, generally, tasks that have more steps, take more time to complete, and involve processing of verbal / character information tend to make the user engage in System 2 type of thinking. On the other hand, tasks that have fewer steps, can be completed quickly, and involve processing of visual / audio information generally tend to make the user engage in System 1 type of thinking. These and other pointers for making a determination or prediction about which type of thinking the user is likely to engage in for a particular task are predefined within the task characteristic module 220 and can be implemented by the electronic control unit 130. As described similarly with respect to the user's state, one or more characteristics of a task do not independently indicate which type of thinking the user will engage in for a particular task.

[0046] Therefore, the electronic control unit 130 may further be configured with a user characteristic module 230 implemented to analyze one or more characteristics of the user to determine whether the task is a task for which the user is likely to implement System 1 type of thinking for essential completion or a task for which the user is likely to implement System 2 type of thinking. That is, the electronic control unit 130 can request and receive information related to a specific user from a database that stores information about the user. The information may include characteristics such as the user's experience level in performing the task, the number of times the user has completed a specific task or a similar task in the past, the time the user has previously spent in solving the same or similar tasks, whether the user is proficient in a series of specific skills correlated with one or more characteristics of the task, and the frequency with which the user interacts with a specific task. As discussed above, the information may be defined in a user profile generated based on the user's previous experience in completing the task and / or based on prompted feedback required by the user's system.

[0047] The user characteristic module 230 provides guidance for the electronic control unit 130 to make a decision or at least a prediction about which type of thinking the user is likely to employ in performing a specific task. As a non-limiting example, when engaged in a system task, a more experienced user in solving a specific task is more likely to employ System 1 type of thinking, while a less experienced user is more likely to employ System 2 type of thinking. The experience level may be defined by the number of times the user has completed this specific task or a similar task. In addition to the experience level, the time the user has previously required to solve the same or similar tasks may also be provided.

[0048] Once information has been generated and / or collected for each of the three factors (e.g., task characteristics, user characteristics, and user state), the electronic control unit 130 combines decisions and / or predictions about which thinking type the user is currently employing based on the analysis through each of the analysis modules 210 - 230. Since none of the factors exclusively identify the thinking type the user will use when engaging in a particular task, the electronic control unit 130 implements another module 240 to consider multiple decisions and make a final decision and / or prediction about whether the user is engaging in System 1 type thinking or System 2 type thinking. As discussed in more detail herein, the electronic control unit 130 can utilize one or more machine learning models trained to digest multi-dimensional information and make decisions and / or predictions about whether the user will engage in System 1 type thinking or System 2 type thinking for a particular task.

[0049] In some embodiments, the decision made by the electronic control unit 130 as an output after implementing each of the analysis modules 210 - 230 can be a decision with a certain confidence value. The confidence value indicates the likelihood that the user is engaging in System 1 type thinking or System 2 type thinking based on the information analyzed by that particular module. The confidence value can be a percentage on a scale of 0% - 100%, with 100% being the strength of the decision made in response to the analysis performed using the particular module.

[0050] Referring to FIG. 3, an exemplary implementation of system 100 deployed within vehicle 110 is depicted to determine whether a user relies on System 1 type of thinking or System 2 type of thinking when engaging in vehicle-based tasks. In some embodiments, a physiological sensor 142 is deployed inside the passenger compartment of the vehicle to capture physiological response data of user 300 (e.g., a driver) when the user 300 engages in various tasks. For example, as depicted, a gaze tracking system 140 is positioned within vehicle 110, and thus a camera or detection device (e.g., 138) may be configured to capture the driver's eye movements, pupil dilation, head and / or body position. In some embodiments, an illumination device 141, such as an infrared lamp, may direct infrared light towards the driver to enhance the detection of eye movements, pupil dilation, head and / or body position. Additionally, one or more physiological sensors 142 may be configured within seat 310 of the vehicle to monitor characteristics of user 300 such as, but not limited to, respiration rate, heart rate, body temperature, etc. In some embodiments, one or more physiological sensors 142 may be implemented in the form of a wearable device 149 communicatively coupled to electronic control unit 130. The user may engage in vehicle-related tasks such as interacting with a navigation system to obtain directions to a destination, changing a playlist on an audio system, reviewing or updating a schedule for a pickup, delivery or appointment, or other tasks. However, it is understood that the implementation of system 100 to determine whether a user uses System 1 type of thinking or System 2 type of thinking when engaging in one task within vehicle 110 is merely one environment in which system 100 may be deployed.

[0051] In some embodiments, the vehicle navigation system can provide instructions to the vehicle's user, and when the user is engaging in System 1 type of thinking, the system can provide simplified visual cues such as arrows indicating which way to go and simplified audible cues such as "turn left" or "turn right". Conversely, when the user is engaging in System 2 type of thinking, the system can provide more detailed information, such as more complex instructions like "exit at Exit 4 onto I-5, stay in the right lane and proceed to Exit 23" and a more detailed map with more detailed audible cues.

[0052] Referring now to FIG. 4, an exemplary schematic diagram of a machine learning model for predicting whether a user uses System 1 type of thinking or System 2 type of thinking when engaged in a task is depicted. Specifically, FIG. 4 depicts a neural network 400 type of machine learning model. However, it should be understood that various types of machine learning models may be implemented by the electronic control unit 130 to predict or determine whether a user uses System 1 type of thinking or System 2 type of thinking when engaged in a task. As used herein, the term "machine learning model" means one or more mathematical models configured to discover patterns in data and apply the determined patterns to new data sets to form predictions. Depending on the nature of the problem to be solved and the type and volume of data, different approaches, also called machine learning categories, are implemented. The categories of machine learning models include, for example, supervised learning, unsupervised learning, reinforcement learning, deep learning, or combinations thereof.

[0053] Referring to, for example, a neural network 400 type of machine learning model, the neural network 400 can include one or more layers 405, 410, 415, 420 having one or more nodes 401 connected by node connections 402. The one or more layers 405, 410, 415, 420 can include an input layer 405, one or more hidden layers 410, 415, and an output layer 420. The input layer 405 represents raw information fed into the neural network 400. For example, one or more characteristics of one or more tasks 152, one or more characteristics of one or more users 154, sensor data from one or more cameras 138, a gaze tracking system 140, and / or physiological response data from one or more physiological sensors 142 can be input into the neural network 400 at the input layer 405. The neural network 400 processes the raw information received at the input layer 405 via the nodes 401 and the node connections 402. The one or more hidden layers 410, 415 perform computational activities according to the inputs from the input layer 405 and the weights for the node connections 402. In other words, the hidden layers 410, 415 perform calculations and transfer information from the input layer 405 to the output layer 420 via the connected nodes 401 and the node connections 402.

[0054] Generally, when neural network 400 is learning, this neural network 400 is identifying and determining patterns within the raw information received at input layer 405. In response, one or more parameters, such as the weights associated with node connections 402 between nodes 401, can be adjusted through a process known as error backpropagation. There are various processes through which learning can occur, but it should be understood that two common learning processes include associative mapping and regularity detection. Associative mapping means a learning process in which neural network 400 learns to generate a specific pattern on an input set whenever another specific pattern is applied to the input set. Regularity detection means a learning process in which neural network 400 learns to respond to specific characteristics of the input pattern. In associative mapping, neural network 400 memorizes the relationships between patterns, while in regularity detection, the response of each unit has a specific "meaning". This type of learning mechanism can be used for feature discovery and knowledge representation.

[0055] A neural network has knowledge stored in the values of its node connection weights. Modification of the knowledge stored in the network as a function of experience implies learning rules for changing the weight values. Information is stored in weight matrix W of neural network 400. Learning is the determination of weights. Depending on the way learning is done, two main neural network categories can be distinguished: namely, 1) fixed networks where the weights cannot be changed (i.e., dW / dt = 0), and 2) adaptive networks where their weights can be changed (i.e., dW / dt ≠ 0). In fixed networks, the weights are fixed a priori according to the problem to be solved.

[0056] To train the neural network 400 to perform a certain task, adjustments to the weights are made in such a way that the error between the desired output and the actual output is reduced. This process may require the neural network 400 to calculate the error weight derivative (EW). In other words, the neural network must calculate how the error changes as each weight increases or decreases slightly. The backpropagation algorithm is one method used to determine the EW.

[0057] The algorithm calculates each EW by first calculating the error derivative (EA), that is, the rate at which the error changes as the activity level of the unit is changed. For the output unit, the EA is simply the difference between the actual output and the desired output. To calculate the EA for a hidden unit in the layer immediately preceding the output layer, first, all the weights between that hidden unit and the output units to which it is connected are identified. Then, these weights are multiplied by the EA of these output units, and the products are added together. This sum is equal to the EA for the selected hidden unit. After calculating the EA for all the hidden units in the hidden layer immediately preceding the output layer, in the same way, the EA for other layers can be calculated while moving layer by layer in the direction opposite to the propagation of activity through the neural network 400, which is why it is called "backpropagation of error". Once the EA has been calculated for a unit, it is easy to calculate the EW for each incoming connection of the unit. The EW is the product of the activity through the incoming connection and the EA. It should be understood that this is only one way to train the neural network 400 to perform a certain task.

[0058] Referring further to FIG. 4, neural network 400 may include one or more hidden layers 410, 415 that feed one or more nodes 401 in output layer 420. Depending on the particular output that neural network 400 is configured to generate, there may be one or more output layers 420. For example, neural network 400 may be trained to output a prediction 430 as to whether a user engages in System 1 type of thinking or System 2 type of thinking when performing a particular task. Further, neural network 400 may similarly generate a confidence value 440 that indicates the likelihood that the user is using System 1 type of thinking or System 2 type of thinking. Additionally, known conditions 450 may be determined by neural network 400 during training and used as feedback for further training neural network 400.

[0059] Turning to FIG. 5, an exemplary flowchart 500 is depicted for determining whether a user uses System 1 type of thinking or System 2 type of thinking when engaging in one task. In other words, flowchart 500 shows an exemplary method implemented by electronic control unit 130 for determining or at least predicting whether a user engages in System 1 type of thinking or System 2 type of thinking when completing a particular task. Flowchart 500 is merely one way that a system may implement for determining or at least predicting whether a user engages in System 1 type of thinking or System 2 type of thinking when completing a particular task. Without departing from the scope of the present disclosure, it is possible to implement other methods that embody the analysis of the three factors (e.g., user state, task characteristics, and user characteristics) as described herein.

[0060] In block 502, the electronic control unit 130 identifies the task the user is performing. As discussed above, the determination can be made based on the current operation of the system that interacts with the user, such as the operation of the navigation system by the user 300 (e.g., the driver) of the vehicle 110. However, in some embodiments, in order to determine which task the user is performing, the image data collected by one or more cameras 138 can be analyzed. For example, one or more cameras 138 can identify a cooking task through image data that captures a user interacting with a cooking appliance, a mixing bowl, etc. Further, in block 502, the electronic control unit 130 can obtain information through the image data about whether the user is performing multiple tasks or is concentrating on a specific task.

[0061] In block 504, the electronic control unit 130 can query a database (e.g., the data storage component 150) having one or more characteristics of one or more tasks 152 about the characteristics related to the identified task (e.g., the specific task the user is engaged in). As a result, the electronic control unit 130 determines one or more characteristics about the identified task based on the information received from the database. In some embodiments, in block 504, the electronic control unit 130 can further analyze one or more characteristics of the identified task to determine whether the task is a task that the user can engage in System 1 type of thinking or a task that the user can engage in System 2 type of thinking, as described, for example, in relation to the task characteristic module 220 based on one or more characteristics.

[0062] In block 506, the electronic control unit 130 can query a database (e.g., data storage component 150) having one or more characteristics of one or more users 154 regarding characteristics related to the user performing the identified task. As a result, the electronic control unit 130 determines one or more characteristics about the user based on the information received from the database. These characteristics can relate to the user's expertise or experience in and / or for completing a particular task. In some embodiments, in block 506, the electronic control unit 130 can further analyze one or more characteristics of the user to determine whether the user is more likely to engage in System 1 type of thinking or the user is more likely to engage in System 2 type of thinking to complete the task, as described, for example, in connection with the user characteristics module 230, based on the one or more characteristics.

[0063] In block 508, the electronic control unit 130 receives one or more signals from one or more sensors, including, for example, one or more cameras 138, a gaze tracking system 140, a physiological sensor 142, etc. The electronic control unit 130 analyzes the signals that may include physiological response data about the user involved in performing the identified task. Decisions regarding stress level, fatigue level, etc. are made via the analysis of one or more signals from one or more sensors by the electronic control unit 130. The electronic control unit 130 determines the user's state via the analysis of one or more physiological sensors configured to detect one or more characteristics of the user. In some embodiments, in block 508, the electronic control unit 130 determines whether the user is engaging in System 1 type of thinking or System 2 type of thinking to complete the task based on the user's state, as described, for example, in connection with the user characteristics module 230.

[0064] In block 510, the electronic control unit 130 compiles the decisions made through the analysis performed in blocks 504 - 508 and makes a determination or at least a prediction, along with a confidence level, as to whether the user uses System 1 type of thinking or System 2 type of thinking when engaging in a task. The determination can be made by weighting three factors (e.g., one or more characteristics of the determined task, one or more characteristics of the determined user, and the determined state of the user). In some embodiments, the electronic control unit 130 may prioritize one factor over another based on the confidence level determined for that factor. That is, if one factor more deterministically indicates System 1 type of thinking or System 2 type of thinking, that factor may be weighted more when combining all three factors in making a determination as to whether the user is using System 1 type of thinking or System 2 type of thinking when engaging in a task.

[0065] Once the system 100 determines the type of thinking the user is using for a task, it is configured to select and implement a communication channel consistent with the type of thinking of the user for the purpose of improving the communication interface between the system 100 and the user. In other words, when the system determines or receives a classification as to whether the user of the system is using System 1 type of thinking or System 2 type of thinking, the system determines the best way to provide information for the user to complete the task that works with the type of thinking the user is currently using. That is, in block 512, the electronic control unit 130 is further configured to select and implement a communication channel corresponding to the determined type of thinking used by the user. For example, when it is determined whether the user mainly uses System 1 type of thinking or System 2 type of thinking to complete a specific task, the selected and implemented communication channel is a communication channel consistent with the type of thinking the user mainly uses to complete the specific task.

[0066] For example, when a user is conducting an investigation and using System 1 type of thinking, the system is considered to provide information using visual objects such as charts and graphs (e.g., what is called the visual mode here) that illustrate the theme of the investigation. Conversely, when a user is utilizing System 2 type of thinking, the system may provide more linguistic information such as a numerical spreadsheet and character information (e.g., what is called the character mode in this specification) regarding the theme of the investigation.

[0067] Furthermore, the thinking type used by the user may change while engaged in the same task. Thus, the system 100 is further configured to continuously assess the thinking type the user is implementing and accordingly update the communication channel. For example, once the electronic control unit 130 first executes block 512, the process returns to block 504 (or optionally block 506, block 508 or block 510) to re-evaluate the user and the thinking type the user is using for the task. Once the process returns to block 510, after first iterating block 510, the process proceeds to block 514. At block 514, the electronic control unit 130 determines whether the determined task type the user is using has changed since the first (or preceding) time the thinking type was determined. If the thinking type has not changed (block 514, NO), the process is also configured to return to block 504 (or optionally block 506, block 508 or block 510) to re-evaluate the user and the thinking type the user is using for the task. However, if the thinking type the user is using for the task has changed (e.g., from system 1 type thinking to system 2 type thinking, or from system 2 type thinking to system 1 type thinking) (block 514, YES), the electronic control unit 130 proceeds to return to block 512 and selects and implements a communication channel corresponding to the determined thinking type the user will use during a subsequent or second time period. The process continues to iterate until the task changes. When the task changes, the process can end or restart at block 502.

[0068] In some embodiments, if the determined thinking type that the user is using for a task is System 1 type of thinking, the implemented communication channel includes a visual communication mode. Similarly, if the determined thinking type that the user is using for a task is System 2 type of thinking, the implemented communication channel includes a text communication mode. However, these are merely some examples. A matrix of communication channels corresponding to the thinking types that the user uses for a defined task can be predefined within the system. Furthermore, it should be understood that the communication channel does not necessarily need to change the medium used to communicate with the user, but only the level of detail provided through the medium changes. For example, for both System 1 type of thinking and System 2 type of thinking, an audible medium or a visual medium can be used, but the level of detail or amount of information provided may be different when combined with System 1 type of thinking and when combined with System 2 type of thinking.

[0069] The functional blocks and / or flowcharts described herein can be translated into machine-readable instructions. By way of non-limiting example, the machine-readable instructions can be written using any programming protocol, such as (i) descriptive text to be parsed (e.g., hypertext markup language, extensible markup language, etc.), (ii) assembly language, (iii) object code generated from source code by a compiler, (iv) source code written using syntax from any suitable programming language for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. Alternatively, the machine-readable instructions may be written in a hardware description language (HDL), such as logic implemented via any of a field programmable gate array (FPGA) configuration or an application specific integrated circuit (ASIC) or their equivalents. Accordingly, the functionality described herein can be implemented in any conventional computer programming language, either as pre-programmed hardware elements or as a combination of hardware and software components.

[0070] It should be understood that the embodiments described herein are directed to systems and methods for determining whether a user uses System 1 type of thinking or System 2 type of thinking when engaging in one task based on three factors. In some embodiments, the systems and methods can receive, analyze, and make decisions or predictions regarding whether a user is engaging System 1 type of thinking or System 2 type of thinking in performing a particular task, using an electronic control unit and / or a neural network 400. For example, a method for determining whether a user uses System 1 type of thinking or System 2 type of thinking when engaging in one task can include determining one or more characteristics of the task based on information about the task received from a database storing information about the task, determining one or more characteristics of the user regarding the task, determining the state of the user based on one or more physiological sensors configured to detect one or more characteristics of the user, and determining whether the user uses System 1 type of thinking or System 2 type of thinking when engaging in the task based on the determined one or more characteristics of the task, the determined one or more characteristics of the user, and the determined state of the user.

[0071] Furthermore, it is further understood that the communication channel used to interface with the user can be selected and implemented based on the determined type of thinking the user is using for the task. For example, in some embodiments, a method for selecting a communication channel based on the type of thinking the user uses for one task includes, during a first time period, based on one or more characteristics of the task, one or more characteristics of the user, and the state of the user based on physiological response data from one or more physiological sensors monitoring the user, determining whether the user is using System 1 type of thinking or System 2 type of thinking for the task; and implementing a communication channel to be used for the task during the first time period corresponding to the determined type of thinking the user is using for the task.

[0072] It is noted that the terms "substantially" or "about" in this specification can be used to represent the inherent uncertainty that may arise from any quantitative comparison, value, measurement, or other representation. These terms are also used in this specification to represent the degree to which a quantitative expression can vary from the stated reference without resulting in a change in the basic function of the subject matter in question.

[0073] Although specific embodiments have been illustrated and described herein, it should be understood that various other changes and modifications can be made without departing from the spirit and scope of the claimed subject matter. Moreover, although various aspects of the claimed subject matter have been described herein, such aspects need not be utilized in combination. Accordingly, the appended claims are intended to cover all such changes and modifications that fall within the scope of the claimed subject matter.

Claims

1. A method for selecting a communication channel based on the type of thinking used by a user for a task, comprising: determining, based on one or more characteristics of the task, one or more characteristics of the user, and the state of the user based on physiological response data from one or more physiological sensors monitoring the user, that the user is using System 1 type of thinking or System 2 type of thinking for the task; implementing a communication channel to be utilized for the task corresponding to the determined type of thinking being used by the user for the task; determining, during a subsequent time interval, that the type of thinking being used by the user for the task is different from that during an initial time interval; implementing, during the subsequent time interval, a different communication channel corresponding to the determined type of thinking being used by the user for the task during the subsequent time interval; wherein the communication channel during the initial time interval includes visual direction cues, and the communication channel during the subsequent time interval includes visualization of an overview map of the environment around the user.

2. The method of claim 1, further comprising selecting the communication channel to be utilized for the task based on a matrix defining the determined type of thinking being used by the user for the task and the communication channel to be implemented for the task.

3. The method of claim 1, wherein when the determined type of thinking being used by the user for the task is System 1 type of thinking, the implemented communication channel includes a visual communication mode.

4. The method of claim 1, wherein when the determined type of thinking being used by the user for the task is System 2 type of thinking, the implemented communication channel includes a text communication mode.

5. identifying the task; determining one or more characteristics of the task based on information about the task received from a database storing information about the task; Determining, based on the information about the user received from a database storing information about the user, one or more characteristics of the user related to the task; The method according to claim 1, further comprising.

6. In a system for selecting a communication channel based on a thinking type used by a user for a task, One or more physiological sensors configured to detect one or more characteristics of the user; An electronic control unit communicatively coupled to the one or more physiological sensors, Based on one or more characteristics of the task, one or more characteristics of the user, and physiological response data from the one or more physiological sensors, determining that the user is using system 1 type of thinking or system 2 type of thinking for the task, Implementing a communication channel to be utilized for the task corresponding to the determined thinking type used by the user for the task, An electronic control unit configured as such; Including, The electronic control unit further: Determining during a subsequent time interval that the thinking type used by the user for the task is different from that during an initial time interval; Implementing a different communication channel corresponding to the determined thinking type used by the user for the task during the subsequent time interval during the subsequent time interval; Configured to do so, The communication channel during the initial time interval includes a visual direction cue, and the communication channel during the subsequent time interval includes a visualization of an overview map of the environment around the user, the system.

7. The electronic control unit further: Selecting the communication channel to be utilized for the task based on a matrix defining the determined thinking type used by the user for the task and the communication channel to be implemented with respect to the task, The system according to claim 6, configured as such.

8. The system according to claim 6, wherein when the determined thinking type used by the user for the task is system 1 type of thinking, the implemented communication channel includes a visual communication mode.

9. The system according to claim 6, wherein when the determined thinking type used by the user for the task is system 2 type thinking, the implemented communication channel includes a text communication mode.

10. The electronic control unit further: identifies the task; determines one or more characteristics of the task based on the information related to the task received from a database storing information related to the task; determines one or more characteristics of the user related to the task based on the information related to the user received from a database storing information related to the user; The system according to claim 6, which is configured to perform the above.

11. In a system for selecting a communication channel based on a thinking type used by a user for a task, one or more physiological sensors configured to detect one or more characteristics of the user; an electronic control unit communicatively coupled to the one or more physiological sensors, implementing a machine learning model, receives, as an input to the machine learning model, one or more characteristics of the task, one or more characteristics of the user related to the task, and the state of the user based on the one or more characteristics of the user detected by the one or more physiological sensors, using the machine learning model, predicts that the user is using system 1 type thinking or system 2 type thinking for the task based on the one or more characteristics of the task, the one or more characteristics of the user, and the state of the user, and implements a communication channel to be used for the task corresponding to the determined thinking type used by the user for the task, an electronic control unit configured as above; including The electronic control unit further: predicts, using the machine learning model, that the thinking type used by the user for the task during a subsequent time interval is different from that during an initial time interval; implements a different communication channel corresponding to the determined thinking type used by the user for the task during the subsequent time interval during the subsequent time interval; is configured to perform the above, The communication channel during the initial time interval includes a visual direction cue, and the communication channel during the subsequent time interval includes a visualization of an overview map of the environment around the user, the system.

12. The electronic control unit further: configured to select the communication channel to be utilized for the task based on a matrix defining the determined type of thinking used by the user for the task and the communication channel to be implemented for the task, the system according to claim 11.

13. The system according to claim 11, wherein when the determined type of thinking used by the user for the task is system 1 type of thinking, the implemented communication channel includes a visual communication mode.

14. The system according to claim 11, wherein when the determined type of thinking used by the user for the task is system 2 type of thinking, the implemented communication channel includes a text communication mode.

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